models.py

from keras import layers
from keras.models import Sequential


def convlstm_model(SEQUENCE_LENGTH, IMAGE_SIZE, CLASSES_LIST):
    '''
    This function will construct the required convlstm model.
    Returns:
        model: It is the required constructed convlstm model.
    '''

    # We will use a Sequential model for model construction
    model = Sequential()

    # Define the Model Architecture.
    ########################################################################################################################

    model.add(layers.ConvLSTM2D(filters=4, kernel_size=(3, 3), activation='tanh', data_format="channels_last",
                                recurrent_dropout=0.2, return_sequences=True, input_shape=(SEQUENCE_LENGTH,
                                                                                           IMAGE_SIZE, IMAGE_SIZE, 3)))

    model.add(layers.MaxPooling3D(pool_size=(1, 2, 2),
              padding='same', data_format='channels_last'))
    model.add(layers.TimeDistributed(layers.Dropout(0.2)))

    model.add(layers.ConvLSTM2D(filters=8, kernel_size=(3, 3), activation='tanh', data_format="channels_last",
                                recurrent_dropout=0.2, return_sequences=True))

    model.add(layers.MaxPooling3D(pool_size=(1, 2, 2),
              padding='same', data_format='channels_last'))
    model.add(layers.TimeDistributed(layers.Dropout(0.2)))

    model.add(layers.ConvLSTM2D(filters=14, kernel_size=(3, 3), activation='tanh', data_format="channels_last",
                                recurrent_dropout=0.2, return_sequences=True))

    model.add(layers.MaxPooling3D(pool_size=(1, 2, 2),
              padding='same', data_format='channels_last'))
    model.add(layers.TimeDistributed(layers.Dropout(0.2)))

    model.add(layers.ConvLSTM2D(filters=16, kernel_size=(3, 3), activation='tanh', data_format="channels_last",
                                recurrent_dropout=0.2, return_sequences=True))

    model.add(layers.MaxPooling3D(pool_size=(1, 2, 2),
              padding='same', data_format='channels_last'))
    # model.add(TimeDistributed(Dropout(0.2)))

    model.add(layers.Flatten())

    model.add(layers.Dense(len(CLASSES_LIST), activation="softmax"))

    ########################################################################################################################

    # Display the models summary.
    model.summary()

    # Return the constructed convlstm model.
    return model


def LRCN_model(SEQUENCE_LENGTH, IMAGE_SIZE, CLASSES_LIST):
    '''
    This function will construct the required LRCN model.
    Returns:
        model: It is the required constructed LRCN model.
    '''

    # We will use a Sequential model for model construction.
    model = Sequential()

    # Define the Model Architecture.
    ########################################################################################################################

    model.add(layers.TimeDistributed(layers.Conv2D(16, (3, 3), padding='same', activation='relu'),
                                     input_shape=(SEQUENCE_LENGTH, IMAGE_SIZE, IMAGE_SIZE, 3)))

    model.add(layers.TimeDistributed(layers.MaxPooling2D((4, 4))))
    model.add(layers.TimeDistributed(layers.Dropout(0.25)))

    model.add(layers.TimeDistributed(layers.Conv2D(
        32, (3, 3), padding='same', activation='relu')))
    model.add(layers.TimeDistributed(layers.MaxPooling2D((4, 4))))
    model.add(layers.TimeDistributed(layers.Dropout(0.25)))

    model.add(layers.TimeDistributed(layers.Conv2D(
        64, (3, 3), padding='same', activation='relu')))
    model.add(layers.TimeDistributed(layers.MaxPooling2D((2, 2))))
    model.add(layers.TimeDistributed(layers.Dropout(0.25)))

    model.add(layers.TimeDistributed(layers.Conv2D(
        64, (3, 3), padding='same', activation='relu')))
    model.add(layers.TimeDistributed(layers.MaxPooling2D((2, 2))))
    model.add(layers.TimeDistributed(layers.Dropout(0.25)))

    model.add(layers.TimeDistributed(layers.Flatten()))

    model.add(layers.LSTM(64, return_sequences=True))
    model.add(layers.LSTM(128))

    model.add(layers.Dense(128))
    model.add(layers.Dropout(0.25))

    model.add(layers.Dense(len(CLASSES_LIST), activation='softmax'))

    ########################################################################################################################

    # Display the models summary.
    model.summary()

    # Return the constructed LRCN model.
    return model